# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='CascadeEncoderDecoder',
    num_stages=2,
    pretrained='open-mmlab://resnet50_v1c',
    backbone=dict(type='ResNetV1c',
                  depth=50,
                  num_stages=4,
                  out_indices=(0, 1, 2, 3),
                  dilations=(1, 1, 2, 4),
                  strides=(1, 2, 1, 1),
                  norm_cfg=norm_cfg,
                  norm_eval=False,
                  style='pytorch',
                  contract_dilation=True),
    decode_head=[
        dict(type='FCNHead',
             in_channels=1024,
             in_index=2,
             channels=256,
             num_convs=1,
             concat_input=False,
             dropout_ratio=0.1,
             num_classes=19,
             norm_cfg=norm_cfg,
             align_corners=False,
             loss_decode=dict(type='CrossEntropyLoss',
                              use_sigmoid=False,
                              loss_weight=0.4)),
        dict(type='OCRHead',
             in_channels=2048,
             in_index=3,
             channels=512,
             ocr_channels=256,
             dropout_ratio=0.1,
             num_classes=19,
             norm_cfg=norm_cfg,
             align_corners=False,
             loss_decode=dict(type='CrossEntropyLoss',
                              use_sigmoid=False,
                              loss_weight=1.0))
    ],
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
